Estimating Racial Disparities When Race is Not Observed
提出一种统计方法，在缺乏个体种族数据时估计种族差异，相比传统方法误差降低84%，并应用于美国房贷利息扣除的种族不平等分析。
Estimating racial disparities without access to individual-level racial information is a common challenge in economic and policy settings. We develop a statistical method that relaxes the strong independence assumption of common race imputation approaches like Bayesian-Improved Surname Geocoding (BISG). Our identification assumption is that surname is conditionally independent of the outcome given (unobserved) race, residence location, and other observed characteristics. The proposed approach reduces error by up to 84% relative to BISG when estimating racial differences in political party registration. In our application, we estimate racial differences in who benefits from the home mortgage interest deduction using individual-level tax data from the U.S. Internal Revenue Service. Our analysis reveals that many fewer Black and Hispanic filers claim the HMID than White and Asian filers. We also find that the racial gaps in homeownership rates alone cannot explain this disparity.